For the first time, artificial intelligence has been used to predict the outcomes of cases heard at a major European court.

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Researchers from the University of Sheffield, the University of Pennsylvania and University College London programmed the machine to analyse text from cases heard at the European Court of Human Rights (ECtHR) and predict the outcome of the judicial decision.

During tests, the AI used a machine learning algorithm to make predictions with 79 per cent accuracy.

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"We don't see AI replacing judges or lawyers, but we think they'd find it useful for rapidly identifying patterns in cases that lead to certain outcomes," explained Dr Nikolaos Aletras, who led the study at UCL Computer Science.

"It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights."

In developing the method, the team found judgements by the ECtHR correlate highly to non-legal facts rather than directly legal arguments, suggesting judges of the Court are, in the jargon of legal theory, 'realists' rather than 'formalists'. This supports findings from previous studies of the decision-making processes of other high-level courts, including the US Supreme Court.

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The team of computer and legal scientists extracted case information published by the ECtHR in their openly accessible database. The researchers identified English language data sets for 584 cases relating to Articles 3, 6 and 8 of the Convention and applied an AI algorithm to find patterns in the text. To prevent bias and mislearning, they selected an equal number of violation and non-violation cases.

Article 3 relates to torture and "inhuman and degrading treatment" and made up 250 of the studied cases. Article 6 protects the right to a fair trial and was relevant in 80 cases, while Article 8 provides a right to respect for one's "private and family life, his home and his correspondence" and was the leading article in 254 of the cases studied.

The most reliable factors for predicting the court's decision were found to be the language used as well as the topics and circumstances mentioned in the case text. The 'circumstances' section of the text includes information about the factual background to the case. By combining information extracted from the abstract 'topics' that the cases cover and 'circumstances' across data for all three articles, an accuracy of 79 per cent was achieved.

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"The study corroborates the findings of other empirical work on the determinants of reasoning performed by high-level courts. It should be further pursued and refined, through the systematic examination of more data," explained co-author Dr Dimitrios Tsarapatsanis, a Lecturer in Law at the University of Sheffield.

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Previous studies have predicted outcomes based on the nature of the crime, or the policy position of each judge. This study is the first time judgements have been predicted using text analysis prepared by the court.

"We expect this sort of tool would improve efficiencies of high-level, in-demand courts, but to become a reality, we need to test it against more articles and the case data submitted to the court," added Dr Vasileios Lampos from UCL.

"Ideally, we'd test and refine our algorithm using the applications made to the court rather than the published judgements, but without access to that data we rely on the court-published summaries of these submissions."